Streaming neural network models for fast frame-wise responses to various speech and sensory signals are widely adopted on resource-constrained platforms. Hence, increasing the learning capacity of such streaming models (i.e., by adding more parameters) to improve the predictive power may not be viable for real-world tasks. In this work, we propose a new loss, Streaming Anchor Loss (SAL), to better utilize the given learning capacity by encouraging the model to learn more from essential frames. More specifically, our SAL and its focal variations dynamically modulate the frame-wise cross entropy loss based on the importance of the corresponding frames so that a higher loss penalty is assigned for frames within the temporal proximity of semantically critical events. Therefore, our loss ensures that the model training focuses on predicting the relatively rare but task-relevant frames. Experimental results with standard lightweight convolutional and recurrent streaming networks on three different speech based detection tasks demonstrate that SAL enables the model to learn the overall task more effectively with improved accuracy and latency, without any additional data, model parameters, or architectural changes.

翻译：面向资源受限平台，用于快速响应各类语音和感知信号的流式神经网络模型被广泛采用。因此，通过增加模型参数来提升此类流式模型的学习能力与预测性能，在实际任务中往往不可行。本文提出一种新型损失函数——流式锚点损失（Streaming Anchor Loss, SAL），通过引导模型从关键帧中更有效地学习，从而更好地利用给定的学习容量。具体而言，SAL及其焦点变体根据对应帧的重要性动态调节帧级交叉熵损失，使语义关键事件时间邻近范围内的帧获得更高的损失惩罚。由此，该损失函数确保模型训练聚焦于预测出现频率较低但与任务高度相关的帧。在三种基于语音的检测任务上，采用标准轻量级卷积与循环流式网络的实验结果表明，无需额外数据、模型参数或架构调整，SAL即可使模型更有效地学习整体任务，并显著提升准确率与延迟性能。